As a scheme for implementing a large-scale neural network by small-scale circuits, an arrangement is conventionally known which executes time-sharing multiplexing by using a conventionally disclosed neuron model to generate a pulse signal (e.g., Japanese Patent Laid-Open No. 5-47870 and Japanese Patent No. 2679730). Another arrangement is also disclosed previously which can solve wiring problems by forming a neuron element by using an element to output a pulse train and a primary storage means (e.g., Japanese Patent Laid-Open No. 6-93249).
In the field of image recognition, a pattern recognition method (method to execute hierarchical template matching) is proposed which implements a recognition processing algorithm specialized to a specific recognition target by using a neural network model whose concept is obtained from the information processing mechanism of a living body (e.g., Japanese Patent Laid-Open No. 60-712, and Fukushima & Miyake, “1982 Neocognitron: A new algorithm for pattern recognition tolerant of deformation and shifts in position, Pattern Recognition, Vol. 15, pp. 455-469).
As an attempt to more faithfully adopt an information processing mechanism based on the neural network of a living body, a neural network model circuit is proposed which transmits and expresses information by a pulse train corresponding to an action potential (e.g., Murray et al., 1991 Pulse-Stream VLSI Neural Networks Mixing Analog and Digital Techniques, IEEE Trans. on Neutral Networks, Vol. 2, pp. 193-204, Japanese Patent Laid-Open Nos. 7-262157, 7-334478, and 8-153148, and Japanese Patent Nos. 2624143 and 2879670).
As a method of recognizing and detecting a specific target by a neural network formed from pulse train generation neurons, there is a scheme using a model of a high order (second or higher order) by Eckhorn et al. which is premised on linking inputs and feeding inputs (e.g., Eckhorn et al. 1990, Feature linking via synchronization among distributed assemblies: Simulation of results from cat cortex, Neural Computation, Vol. 2, pp. 293-307), i.e., a pulse coupled neural network (to be abbreviated as PCNN hereinafter) (e.g., U.S. Pat. No. 5,664,065, and Broussard, et al. 1999, Physiologically Motivated Image Fusion for Object Detection using a Pulse Coupled Neural Network, IEEE Trans. on Neural Networks Vol. 10, pp. 554-563).
Of the neural networks according to the prior arts, the arrangement which executes time-sharing multiplexing has a problem that the processing time increases along with an increase in number of synapse connections. The remaining arrangements can hardly be implemented as an electronic circuit because the increase in circuit scale and the wiring problems become conspicuous as the number of synapse connections increases, and the dynamic range of information (e.g., weight sum value) to be held increases as signals are added through synapse connections.